Perhaps no trend in contemporary communication system development is more pronounced than the trend towards higher data rates. Significant development in that regard may be seen in the evolution of wireless communication systems, such as cellular communication networks. For example, significant efforts to increase communication data rates can be seen in Direct Sequence Code Division Multiple Access (DS-CDMA) systems such as Wideband CDMA (WCDMA) with its High Speed Packet Access (HSPA) services, and 1X Evolution Data Only (1XEV-DO) systems with their high-rate packet data services.
Sophisticated receiver design represents a key enabling technology for achieving higher data rates at acceptable performance levels in these types of communication systems. Equalization to combat self interference (inter-symbol interference or “ISI”) is needed in dispersive channel environments, for example, to achieve reasonable performance. Linear equalization, such as represented by receiver architectures using “Generalized Rake” (G-Rake) or “Chip Equalization” (CE) processes, becomes insufficient in combating ISI as data rates increase. Newer receiver architectures are thus adopting non-linear equalization processes, such as Decision Feedback Equalization (DFE).
Further, with regard to multi-cell communications, such as found in a WCDMA-based cellular communication network, the presence of high-rate (high-power) transceivers in adjacent cells represents a potentially significant source of Co-Channel Interference (CCI), which also must be suppressed effectively for good receiver performance. The potential lack of signal information complicates the task of CCI suppression. For example, if the other-cell interfering transceivers are not operating in the “active set” of a given transceiver, the given transceiver generally will not have information about the channel delays and coefficients associated with these sources of interference.
If the other-cell users are not in the active set, then information such as channel delays and coefficients will not be available for these sources of interference. As a result, some form of “blind” or non-parametric estimation is needed to address this form of interference.
In general, multiple sources of interference need to be addressed. For complexity reasons, it is convenient to preprocess (pre-equalize) the signal to address one form of interference (e.g. CCI) before processing the signal to address another form of interference (e.g. ISI). Ideally, for performance, the pre-equalizer should only suppress CCI, leaving the ISI alone. Thus, one challenge for the pre-equalizer is to suppress one form of interference but not the other.